from emodel_generalisation.mcmc import load_chains, plot_corner
import numpy as np
from bluepyparallel import init_parallel_factory
import yaml
from emodel_generalisation.model.modifiers import synth_soma, synth_axon
from functools import partial
from emodel_generalisation.model.access_point import AccessPoint
from datareuse import Reuse
from emodel_generalisation.model.evaluation import feature_evaluation
from emodel_generalisation.mcmc import save_selected_emodels
from itertools import cycle
import json
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
from pathlib import Path
from bluepyopt.ephys.responses import TimeVoltageResponse
import matplotlib.pyplot as plt
import pickle
from emodel_generalisation.utils import get_combo_hash
import extra_features


def plot_traces(trace_df, trace_path="traces", pdf_filename="traces.pdf"):
    """Plot traces from df, with highlighs on rows with trace_highlight = True.

    Args:
        trace_df (DataFrame): contains list of combos with traces to plot
        trace_path (str): path to folder with traces in .pkl
        pdf_filename (str): name of pdf to save
    """
    COLORS = cycle(["r"] + [f"C{i}" for i in range(10)])
    trace_df = trace_df.copy()  # prevents annyoing panda warnings
    if "trace_highlight" not in trace_df.columns:
        trace_df["trace_highlight"] = True
    for index in trace_df.index:
        if trace_df.loc[index, "trace_highlight"]:
            c = next(COLORS)

        if "trace_data" in trace_df.columns:
            trace_path = trace_df.loc[index, "trace_data"]
        else:
            combo_hash = get_combo_hash(trace_df.loc[index])
            trace_path = Path(trace_path) / ("trace_id_" + str(combo_hash) + ".pkl")

        with open(trace_path, "rb") as f:
            trace = pickle.load(f)
            if isinstance(trace, list):
                trace = trace[1]  # newer version the response are here
            for protocol, response in trace.items():

                if protocol == "Step_ReboundBurst_burst.soma.v":
                    if isinstance(response, TimeVoltageResponse):
                        plt.figure(protocol, figsize=(30, 7))
                        plt.gca().set_xlim(4500, 9000)
                        plt.plot(response["time"], response["voltage"], c="k", lw=1)

    with PdfPages(pdf_filename) as pdf:
        for fig_id in plt.get_fignums():
            fig = plt.figure(fig_id)
            plt.legend(loc="best")
            plt.suptitle(fig.get_label())
            pdf.savefig()
            plt.close()


if __name__ == "__main__":
    parallel_factory = init_parallel_factory("multiprocessing")
    # parallel_factory = init_parallel_factory("dask_dataframe")
    mcmc_df = load_chains("../../../mcmc_run/run_df.csv", base_path="../../../mcmc_run/")
    mcmc_df = mcmc_df[mcmc_df.cost < 2.0].reset_index(drop=True)
    _mcmc_df = mcmc_df.drop(
        columns=[
            c
            for c in mcmc_df.columns
            if (c[0] in "normalized_parameters")
            and (c[1] not in ["g_pas.all", "gbar_ican.basal", "gcabar_it2.basal"])
        ]
    )

    mask_tonic = mcmc_df["features"]["Step_ReboundBurst_burst.soma.v.tonic_after_burst"] > 0
    print(len(mcmc_df[mask_tonic]))
    plt.figure()
    plt.hist(
        mcmc_df[mask_tonic]["features"]["Step_ReboundBurst_burst.soma.v.tonic_after_burst"], bins=50
    )
    plt.savefig("hist_tonic.pdf")
    plt.close()
    plot_corner(mcmc_df[mask_tonic], filename="corner_tonic.pdf")
    plot_corner(mcmc_df, filename="corner_all.pdf")
    plt.close("all")

    access_point = AccessPoint(
        emodel_dir="../../mcmc_run",
        recipes_path="../../mcmc_run/config/recipes.json",
        final_path="../figure_2/final_tonic.json",
        with_seeds=True,
    )
    exemplar_data = yaml.safe_load(open("../../../mcmc_run/exemplar_data.yaml"))
    access_point.morph_path = exemplar_data["paths"]["all"]
    access_point.settings["morph_modifiers"] = [
        partial(synth_soma, params=exemplar_data["soma"], scale=1.0),
        partial(synth_axon, params=exemplar_data["ais"]["popt"], scale=1.0),
    ]

    df = pd.DataFrame()
    params = np.array([0.0, 0.25, 0.5, 0.75, 1.0]) * 0.0001831686387712
    for i, param in enumerate(params):
        df.loc[i, "name"] = f"tonic_{i}"
        df.loc[i, "emodel"] = "simplest"
        df.loc[i, "new_parameters"] = json.dumps({"gbar_ican.basal": param})
    print(df)
    Path("traces").mkdir(exist_ok=True)
    with Reuse("eval.csv") as reuse:
        df = reuse(
            feature_evaluation,
            df,
            access_point,
            parallel_factory=parallel_factory,
            trace_data_path="traces",
            # record_ions_and_currents=True,
        )
    for i in df.index:
        plot_traces(df.loc[[i]], pdf_filename=f"traces_{i}.pdf")
    plt.close("all")
